We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| caelan/stripstream | canonical | 3 of 4 |
| caelan/pddlstream | extension | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_predicates | Ran | caelan/pddlstream/pddlstream/algorithms/advanced.py pointer only (licence: GPL-3.0) · get_code("f9962248d6dc12ab") |
| header | Ran | caelan/stripstream/stripstream/utils.py code served (permissive licence) · get_code("891df9c3a435284a") |
| implies | Ran | caelan/stripstream/stripstream/utils.py code served (permissive licence) · get_code("6dd6a673e87e8988") |
| separator | Ran | caelan/stripstream/stripstream/utils.py code served (permissive licence) · get_code("915fe54f143e39cc") |
| retrace | Not yet run | caelan/stripstream/stripstream/algorithms/search/bfs.py code served (permissive licence) · get_code("726e392064910bab") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional representation. We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints. The outputs of these generators interact with actions through possibly infinite streams of objects and static predicates. We provide two algorithms which both reduce STRIPStream problems to a sequence of finite-domain planning problems. The representation and algorithms are entirely domain independent. We demonstrate our framework on simple illustrative domains, and then on a high-dimensional, continuous robotic task and motion planning domain.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1701.00287")
get_code_for_paper("1701.00287")
have("1701.00287")
Connect an agent — have() is free.